Data Scientist

We are looking for a data-driven and impact-oriented Credit Risk Data Scientist to join our high-performance fintech team. This role sits at the intersection of risk, product, and growth, where you will design and deploy intelligent credit decisioning systems that directly influence business outcomes.

What we expect you to do:

• Build, validate, and deploy real-time credit risk and fraud detection models to support underwriting decisions
• Work with large-scale, imbalanced datasets to extract meaningful risk insights
• Develop thin-file / new-to-credit models using alternative data sources (mobile, transactional, behavioural signals)
• Optimize credit underwriting strategies including approval rates, credit limits, and pricing decisions
• Design and execute A/B experiments to improve portfolio performance while maintaining risk thresholds
• Continuously monitor model performance, stability, and drift; recommend recalibration strategies
• Collaborate with Product, Engineering, and Growth teams to integrate models into scalable APIs and decision systems
• Build and maintain automated data pipelines and model retraining workflows
• Balance risk vs. growth trade-offs, aligning with business objectives in a fintech lending environment
• Translate complex analytical outputs into clear business recommendations for stakeholders

Requirements

What you bring to the table:

• 2–5 years of experience in credit risk analytics, lending analytics, or fintech data science.
• Master’s degree in a quantitative field such as Statistics, Computer Science, Economics, Applied Mathematics, or related discipline.
• Hands-on experience in statistical modeling, machine learning, and predictive analytics.
• Strong ability to work with messy, real-world datasets (incomplete, noisy, biased) and large-scale data processing.
• Experience in small business lending, fintech, or alternative credit ecosystems.
• Familiarity with model governance, validation frameworks, and explainability techniques (e.g., SHAP).
• Exposure to cloud environments (AWS) and modern data engineering workflows.

Technical Skills Required:

• SQL & Snowflake for data extraction, transformation, and large-scale querying.
• Python for modeling, automation, and data analysis (Pandas, NumPy, Scikit learn, etc.).
• Tableau (or similar BI tools) for data visualization and stakeholder reporting.
• Understanding of ML lifecycle (training, validation, deployment, monitoring).
• Exposure to API integration and production-level model deployment.